Learning-Based Hashing for ANN Search: Foundations and Early Advances

• URL: http://arxiv.org/abs/2510.04127v1
• Date: Sun, 05 Oct 2025 09:59:56 GMT
• Title: Learning-Based Hashing for ANN Search: Foundations and Early Advances
• Authors: Sean Moran,
• Abstract summary: Hashing-based methods provide an efficient solution by mapping high-dimensional data into compact binary codes.<n>Over the past two decades, a substantial body of work has explored learning to hash, where projection and quantisation functions are optimised from data.<n>This article offers a foundational survey of early learning-based hashing methods, with an emphasis on the core ideas that shaped the field.
• Score: 0.5279475826661642
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Approximate Nearest Neighbour (ANN) search is a fundamental problem in information retrieval, underpinning large-scale applications in computer vision, natural language processing, and cross-modal search. Hashing-based methods provide an efficient solution by mapping high-dimensional data into compact binary codes that enable fast similarity computations in Hamming space. Over the past two decades, a substantial body of work has explored learning to hash, where projection and quantisation functions are optimised from data rather than chosen at random. This article offers a foundational survey of early learning-based hashing methods, with an emphasis on the core ideas that shaped the field. We review supervised, unsupervised, and semi-supervised approaches, highlighting how projection functions are designed to generate meaningful embeddings and how quantisation strategies convert these embeddings into binary codes. We also examine extensions to multi-bit and multi-threshold models, as well as early advances in cross-modal retrieval. Rather than providing an exhaustive account of the most recent methods, our goal is to introduce the conceptual foundations of learning-based hashing for ANN search. By situating these early models in their historical context, we aim to equip readers with a structured understanding of the principles, trade-offs, and open challenges that continue to inform current research in this area.

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